JANUS conditions Vision Transformer embeddings on macro-radiomic priors via anatomically guided gating, reaching macro-AUROC 0.88 on an internal test set of 5082 cases and 0.87 on an external set of 2000 cases while improving calibration and reducing high-confidence false positives under domainshift
Radiology: Artificial Intelligence 5(5), e230024 (2023)
4 Pith papers cite this work. Polarity classification is still indexing.
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CT-guided voxel-wise regularization for the displacement field improves whole-body cross-tracer PET registration over global regularization baselines on a 296-patient dataset.
A bidirectional cross-attention point transformer deforms a heart atlas into a 3D four-chamber mesh from sparse cardiac MRI point clouds using locally affine diffeomorphic flows.
CA-GCL combines global contrastive learning with permutation-invariant text augmentation to deliver zero-shot 3D medical abnormality detection that is more robust to prompt changes than prior FVLP methods.
citing papers explorer
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JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift
JANUS conditions Vision Transformer embeddings on macro-radiomic priors via anatomically guided gating, reaching macro-AUROC 0.88 on an internal test set of 5082 cases and 0.87 on an external set of 2000 cases while improving calibration and reducing high-confidence false positives under domainshift
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CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration
CT-guided voxel-wise regularization for the displacement field improves whole-body cross-tracer PET registration over global regularization baselines on a 296-patient dataset.
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Bi-PT: Bidirectional Cross-Attention Point Transformers for Four-Chamber Heart Reconstruction from Sparse Cardiac MRI Data
A bidirectional cross-attention point transformer deforms a heart atlas into a 3D four-chamber mesh from sparse cardiac MRI point clouds using locally affine diffeomorphic flows.
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CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding
CA-GCL combines global contrastive learning with permutation-invariant text augmentation to deliver zero-shot 3D medical abnormality detection that is more robust to prompt changes than prior FVLP methods.